Is Jupyter Notebook Still the King of ML/AI?

Machine learning and artificial intelligence are evolving at a rapid pace—blink and you might miss it! As a result, the Integrated Development Environment (IDE) you choose can significantly impact your workflow and outcomes. Your IDE is more than just a text editor; it’s your command centre, your laboratory, and the space where your data-driven ideas come to life.

I remember my early days in this field, hopping between several IDE options before finally settling on a setup that fit my needs. It’s a common debate among developers, with many passionately defending their tool of choice.

To get a feel for current preferences in the ML/AI community, I conducted a poll on LinkedIn, asking fellow practitioners and enthusiasts: "What's the BEST Python IDE for ML/AI?" The response was fantastic—3,626 votes and a flood of insightful comments! While broader surveys, like those from Stack Overflow and JetBrains, provide useful context, my poll specifically targeted the ML/AI workflow.

So, what did I find? Jupyter Notebook came out on top, with 50% of respondents naming it as their go-to Python IDE. However, a lively discussion followed: is Jupyter really the best choice, especially with powerful alternatives like VS Code gaining traction? Let's dive into the poll results, explore the reasons behind these preferences, and weigh the strengths and weaknesses of each IDE.

The Jupyter Notebook Phenomenon (50%)

Jupyter Notebook isn’t your typical coding environment; it’s a web-based platform that allows you to weave together code, visualisations, equations, and text in a single document. This unique combination has established it as a vital tool in the realm of data science.

Key Strengths:

• Interactive Exploration & Visualisation: Jupyter truly shines when it comes to exploring data. You can run your code step by step, witnessing results unfold in real-time, which allows you to adjust your methods on the go. This hands-on approach is especially invaluable during tasks like cleaning data, engineering features, and testing models, where immediate feedback is essential.
• Reproducibility & Collaboration: One of the standout features of Jupyter Notebooks is their ability to enable reproducible research.What that mean? You can combine code, results, and notes in one place, making it easier for others in your team to replicate your analysis and collaborate more effectively.
• Seamless Library Integration: Jupyter works well with core Python libraries essential for machine learning and artificial intelligence, such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. This integration makes Jupyter a natural choice for anyone looking to get their head into data science.

Challenges:

• AI Coding Assistance: A significant drawback is the difficulty in setting up but surmountable inline AI coding suggestions (like GitHub Copilot or Cursor) within Jupyter. This feature, now common in other IDEs, can significantly boost coding efficiency.
• Version Control & Code Reusability: While manageable, version control and code reuse can be more challenging with notebooks compared to traditional code files.
• Debugging & Large Projects: Although Jupyter offers debugging, yet, it's generally less robust than in full-fledged IDEs. Thus for very large, complex projects, other IDEs might be more suitable.

The JetBrains Python Developers Survey 2022 showed 52% using Jupyter Notebook, supporting these findings.

VS Code - The Rising Contender (31%)

VS Code is a lightweight, yet powerful code editor that's gaining rapid adoption in the ML/AI community. Its extensive extension library allows for seamless customisation, turning it into a tailored development environment for a wide range of needs. The poll showed that 31% of the voters prefer VS Code. Although going by the comments, it's likely more popular than the result suggests.

Key Strengths:

• Extensibility & Customisation: VS Code's marketplace is brimming with extensions, including top-notch support for Python, native Jupyter Notebook integration, Git, debugging, and various ML/AI frameworks. You can customise it precisely to your needs.
• Performance & Remote Development: It's fast and efficient, even with large files. Plus, its ability to connect to remote servers or containers is a game-changer for resource-intensive ML/AI tasks.
• AI-Powered Coding: This is where VS Code truly shines. With extensions such as GitHub Copilot and Cursor offer intelligent code completion, generation, and even context-aware suggestions across multiple files, a massive productivity boost.

Potential Drawbacks:

• Learning Curve: For complete beginners, VS Code might have a slightly steeper learning curve than Jupyter.
• Initial Setup: Configuring extensions and the environment can take a bit of initial effort. But with help in hands, this can be easily overcome.

The Stack Overflow Developer Survey 2023 shows Visual Studio Code as the most popular IDE, with 73.71% of respondents using it, demonstrating its broad appeal across all development domains.

PyCharm - The Professional's Choice (17%)

PyCharm, from JetBrains, is a full-fledged, professional IDE built specifically for Python. It's known for its robust features and comprehensive support for various Python frameworks.

Key Strengths:

• Advanced Debugging & Refactoring: PyCharm boasts a powerful debugger and intelligent code refactoring tools, invaluable for complex projects.
• Database Integration & Project Management: It offers built-in tools for database interaction and excels at managing large, structured projects.

Potential Drawbacks:

• Resource Intensive & Cost: It can be more resource-heavy than VS Code or Jupyter, and the full-featured Professional edition requires a paid license (though a free Community edition exists).
• Less Interactive: Its focus on structured development makes it less ideal for the highly iterative, exploratory style that Jupyter facilitates.

The JetBrains Python Developers Survey 2022 showed 24% use PyCharm.

Sublime Text - The Minimalist Option (1%)

Sublime Text, a fast and lightweight text editor, caters to a niche audience within the ML/AI community. Sure it's only 1% share of the votes. But this shouldn't be seen as inferior, but rather as an indication of its specialised appeal.

Why the Niche Appeal?

• Speed and Customisability: Sublime Text is exceptionally fast and highly customisable, attracting power users who value efficiency and control. Sublime Text is your perfect companion for quick and sharp script edits. Not only that but prototyping, or instances where you need a lightweight, responsive editor.
• Lacks Default Features: It lacks the out-of-the-box features made for ML/AI.

Interpreting the Poll Results and the Comments

Jupyter's enduring appeal lies in its ease of use, especially for those starting in data science, and its strengths in interactive exploration and visualisation. However, VS Code's 31% share, combined with comments praising its Jupyter integration and AI capabilities, positions it as a wonderful contender. The ability to blend a full IDE's power with notebooks' interactivity is a compelling draw.

Advice for Newcomers to the Field

If you're just starting your ML/AI journey, the IDE choice can feel overwhelming. Here's some advice:

• Start with Jupyter: It's an excellent starting point for learning the fundamentals. Its interactive nature lets you experiment, visualise results, and grasp core concepts effectively.
• Transition to VS Code: As you tackle more complex projects, consider VS Code. Its extensions, debugging tools, and AI-powered coding assistance will significantly boost your productivity.
• Consider PyCharm for Large Projects: For large, collaborative projects, explore PyCharm's robust project management and debugging features.
• Experiment and Find Your Fit: The best IDE is the one that best suits your workflow. Experiment, try different options, and see what clicks. Look for online communities, workshops, or even local meetups focused on data science tools – these can be great resources for learning and getting advice; so seek that you may find.

Departing Thoughts:

The poll and the lively discussion it generated highlight the dynamic landscape of Python IDEs for ML/AI. And so here we are. Whilst Jupyter remains the top choice among data professionals, VS Code is quickly emerging as a strong alternative, especially with its growing AI capabilities. PyCharm caters specifically to Python developers, while Sublime Text maintains a loyal, albeit smaller, user base.

Unsurprisingly to say, there's no single "best" IDE. The optimal choice depends on your individual needs, project requirements, and the ever-evolving landscape of data science tools.

What does the future hold for IDEs in the age of AI?

Will AI-powered coding assistants become so common that the choice of IDE becomes less critical?

Share your anticipation and experiences in the comments below!

Let's continue this conversation and learn from each other.